Following up on my previous question, talking with Rashida I found that the problem is a little bit different, so I'm reposting: I’m failing to load the champion version of a wrapper SklearnPipeline model registered in MLFlow. I want to save many experiments to MLFlow and to be able to load the champion version for other downstream pipelines. My catalog.yml looks like this:

model:
 type: kedro_mlflow.io.models.MlflowModelTrackingDataset
 flavor: mlflow.sklearn
 save_args:
  registered_model_name:model

model_loader:
 type: kedro_mlflow.io.models.MlflowModelRegistryDataset
 flavor: mlflow.sklearn
 model_name: "model"
 alias: "champion"

If I try to load the model in a new kedro session, it will demand a run_id. If I try to use the model_loader. It will complain that the model (the wrapper SklearnPipeline object) don’t have a metadata attribute, giving this error message:

│ /opt/anaconda3/envs/topazDS_2/lib/python3.11/site-packages/kedro_mlflow/io/models/mlflow_model_r │
│ egistry_dataset.py:98 in _load                                                                   │
│                                                                                                  │
│    95 │   │   # because the same run can be registered under several different names             │
│    96 │   │   #  in the registry. See <https://github.com/Galileo-Galilei/kedro-mlflow/issues/5>   │
│    97 │   │   import pdb; pdb.set_trace()                                                        │
│ ❱  98 │   │   <http://self._logger.info|self._logger.info>(f"Loading model from run_id='{model.metadata.run_id}'")          │
│    99 │   │   return model                                                                       │
│   100 │                                                                                          │
│   101 │   def _save(self, model: Any) -> None:                                                   │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
AttributeError: 'SklearnPipeline' object has no attribute 'metadata'

DatasetError: Failed while loading data from dataset 
kedro_mlflow.io.models.mlflow_model_registry_dataset.MlflowModelRegistryDataset(model_uri='models:/mill1_west_no_we
nco_st_model@champion', model_name='mill1_west_no_wenco_st_model', alias='champion', flavor='mlflow.sklearn', 
pyfunc_workflow='python_model').
'SklearnPipeline' object has no attribute 'metadata'

I think that the MlflowModelRegistryDataset class wasn't expecting the model to be a sklearn object. Probably there's a difference in how I'm saving the model (MlflowModelTrackingDataset) and how I'm loading it (MlflowModelRegistryDataset). How I could load the champion model? @Rashida Kanchwala @Ravi Kumar Pilla

As discussed, I will look at this and get back to you. In the meantime, @Yolan Honoré-Rougé if you have any suggestions, please let us know. Thank you

Can you open an issue on GitHub to keep track? I'll publish a fix in 2 weeks, but if someone can open a PR I can review and release. The simplest short term solution is to comment out logging, but I'll dig deeper when I have time

okay, I can open an issue and also a short term fix PR conditionally logging if there is no schema constraint for the model

3.11

from the logs looks like py311, can you install 0.13.2

Thank you very much for the PR @Ravi Kumar Pilla . Can you upgrade to kedro 1.0 @Thiago Valejo? The fix will only be available for kedro-mlflow>=1.0.0 which is only compatible with kedro>=1.0 unfortunately :/

He is on Kedro 1.0 when we spoke today

The fix is on pypi, you can upgrade: https://pypi.org/project/kedro-mlflow/

Thanks for the quick turnaround @Yolan Honoré-Rougé @Thiago Valejo please let us know if you have further issues. Thank you

Working as a charm. Thanks a lot @Ravi Kumar Pilla @Rashida Kanchwala and @Yolan Honoré-Rougé